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• 52 abstract "Biodiversity management requires the usage of heterogeneous biological information from multiple sources. Indexing, aggregating, and finding such information is based on names and taxonomic knowledge of organisms. However, taxonomies change in time due to evolution, new scientific findings, opinions of authorities, and changes in our conception about life forms. Furthermore, organism names and their meaning change in time, different authorities use different scientific names for the same taxon in different times, and various vernacular names are in use in dif- ferent languages. This makes data integration and information retrieval difficult without detailed biological information. This paper introduces a meta-ontology for managing the names and taxonomies of organisms, and presents three applications for it: 1) publishing biological species lists as ontology services (ca. 20 taxonomies including more than 80,000 names), 2) collaborative management of the vernacular names of vascu- lar plants (ca. 26,000 taxa), and 3) management of individual scientific name changes based on research results, covering a group of beetles. The applications are based on the databases of the Finnish Museum of Natural History and are used in a living lab environment on the web.".
• 52 hasAuthorList authorList.
• 52 isPartOf proceedings.
• 52 keyword "biology".
• 52 keyword "dynamic ontologies".
• 52 keyword "ontology services".
• 52 keyword "organism names".
• 52 keyword "taxonomy".
• 52 title "Biological Names and Taxonomies on the Semantic Web -- Managing the Change in Scientific Conception".
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• item-1 hasContent jouni-tuominen.
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• item-2 hasContent nina-laurenne.
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• item-3 hasContent eero-hyvoenen.
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• 56 creator darko-anicic.
• 56 creator ljiljana-stojanovic.
• 56 creator nenad-stojanovic.
• 56 creator yongchun-xu.
• 56 type InProceedings.
• 56 label "An approach for the more efficient energy consumption based on the real-time situational awareness".
• 56 sameAs 56.
• 56 abstract "In this paper we present a novel approach for achieving energy efficiency in public building (especially sensor-enabled offices) based on the application of the intelligent complex event processing and semantic technologies. In the nutshell of the approach is an efficient method for realizing real-time situational awareness that helps in recognizing, in real-time, the situations where a more efficient energy consumption is possible and reaction on those opportunities promptly. Semantics allows a proper contextualization of the sensor data (its abstract interpretation) whereas complex event processing enables the efficient real-time processing of sensor data and its logic-based nature supports a declarative definition of the situations of interests. The approach has been implemented in the iCEP framework for the intelligent Complex Event Reasoning, developed by authors. The results from a preliminary evaluation study are very promising: the approach enables a very precise real-time detection of the office occupancy situations that limit the operation of the lighting system based on the actual use of the space".
• 56 hasAuthorList authorList.
• 56 isPartOf proceedings.
• 56 keyword "Complex Event Processing,".
• 56 keyword "Energy Efficiency,".
• 56 keyword "Office Occupancy Control".
• 56 keyword "Semantic technology,".
• 56 title "An approach for the more efficient energy consumption based on the real-time situational awareness".
• authorList type List.
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• item-1 hasContent yongchun-xu.
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• item-2 hasContent nenad-stojanovic.
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• item-3 hasContent ljiljana-stojanovic.
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• item-4 hasContent darko-anicic.
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• 4 creator anna-gossen.
• 4 creator thanh-tran.
• 4 creator veli-bicer.
• 4 type InProceedings.
• 4 label "Relational Kernel Machines for Learning from Graph-structured RDF Data".
• 4 sameAs 4.
• 4 abstract "The amount of semantic data available as RDF is large and increasing. Despites the increased awareness that exploiting this large amount of data requires not only logic-based reasoning but also statistics-based inference capabilities, only little work can be found for the latter. On semantic data, supervised approaches particularly kernel-based Support Vector Machines (SVM) are promising. However, obtaining the right features to be used in kernels is an open problem because the amount of features that can be extracted from the complex structure of semantic data might very large. Further, instead of a single one, combining several kernels that specialize on subsets of features can help to deal with eciency and data sparsity. This creates the additional problem of identifying and combining dierent subset of features and kernels, respectively. In this work, we solve these two problems by employing the strategy of dynamic propositionalization to compute a hypothesis, representing the relevant features for a set of examples. Then, an R-convolution kernel is obtained from a set of clause kernels derived from components of the hypothesis. The learning of the hypothesis and kernel(s) is performed in an interleaving fashion, using a coevolution-based genetic algorithm for the underlying problem of multi-objective optimization. Based on experiments on real-world datasets, we show that the resulting relational kernel machine improves the SVM baseline".
• 4 hasAuthorList authorList.
• 4 isPartOf proceedings.
• 4 keyword "Genetic Optimization".
• 4 keyword "ILP".
• 4 keyword "Kernel Machines".
• 4 keyword "RDF".
• 4 keyword "Relational Learning".
• 4 title "Relational Kernel Machines for Learning from Graph-structured RDF Data".
• authorList type List.
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• item-1 type ListItem.
• item-1 hasContent veli-bicer.
• item-1 next item-2.
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• item-2 hasContent thanh-tran.
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• item-3 type ListItem.
• item-3 hasContent anna-gossen.
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• 6 creator jens-lehmann.
• 6 creator lorenz-buehmann.
• 6 type InProceedings.
• 6 label "AutoSPARQL: Let Users Query Your Knowledge Base".
• 6 sameAs 6.
• 6 abstract "An advantage of Semantic Web standards like RDF and OWL is their flexibility in modifying the structure of a knowledge base. To turn this flexibility into a practical advantage, it is of high importance to have tools and methods, which offer similar flexibility in extracting information from a knowledge base. This is closely related to the ability to easily formulate queries over those knowledge bases. We explain benefits and drawbacks of existing techniques in achieving this goal and then present the QTL algorithm, which fills a gap in research and practice. It uses supervised machine learning and allows users to ask queries without knowing the schema of the underlying knowledge base beforehand and without expertise in the SPARQL query language. We then present the AutoSPARQL user interface, which implements an active learning approach on top of QTL. Finally, we present an evaluation based on the SPARQL query log of the DBpedia knowledge base.".
• 6 hasAuthorList authorList.
• 6 isPartOf proceedings.

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